diff --git a/.github/actions/setup/action.yaml b/.github/actions/setup/action.yaml new file mode 100644 index 0000000..488a190 --- /dev/null +++ b/.github/actions/setup/action.yaml @@ -0,0 +1,11 @@ +runs: + using: composite + steps: + - name: "Install UV" + shell: bash + run: | + curl -LsSf https://astral.sh/uv/install.sh | sh + - name: "Install libsndfile" + shell: bash + run: | + sudo apt-get install libsndfile1 python3-distutils \ No newline at end of file diff --git a/.github/workflows/lint_and_test.yml b/.github/workflows/lint_and_test.yml index c73d227..6cbaa44 100644 --- a/.github/workflows/lint_and_test.yml +++ b/.github/workflows/lint_and_test.yml @@ -1,135 +1,52 @@ -# This workflow will install Python dependencies, run tests and lint with a variety of Python versions -# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python - -name: Python package +name: Lint Python Code on: + # Trigger the workflow on push to master or any pull request push: - branches: ["main"] pull_request: - branches: ["main"] + branches: + - main jobs: - linter: + lock_file: runs-on: ubuntu-latest - strategy: - fail-fast: false - matrix: - python-version: ["3.8"] - steps: - - uses: actions/checkout@v3 - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v3 - with: - python-version: ${{ matrix.python-version }} - cache: "pip" - - name: Install dependencies - run: | - sudo apt-get install libsndfile1 - python -m pip install --upgrade pip - python -m pip install fairseq2n==0.3.0rc1+cpu --pre --extra-index-url https://fair.pkg.atmeta.com/fairseq2/whl/rc/pt2.5.1/cpu/ - python -m pip install fairseq2==0.3.0rc1 --pre --extra-index-url https://fair.pkg.atmeta.com/fairseq2/whl/rc/pt2.5.1/cpu/ - python -m pip install -r requirements.txt - python -m pip install -r requirements-dev.txt - python -m pip install -e . - - name: Lint with flake8 - run: | - # stop the build if there are Python syntax errors or undefined names - flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics - # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide - flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - - name: isort - run: isort --check --diff . - - name: black - run: black --check --diff . - - mypy: + - uses: actions/checkout@v4 + - uses: ./.github/actions/setup + - run: uv lock --locked + linting: runs-on: ubuntu-latest - strategy: - fail-fast: false - matrix: - python-version: ["3.10"] + needs: [lock_file] steps: - - uses: actions/checkout@v3 - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v3 - with: - python-version: ${{ matrix.python-version }} - cache: "pip" - - name: Install dependencies - run: | - sudo apt-get install libsndfile1 - python -m pip install --upgrade pip - python -m pip install fairseq2n==0.3.0rc1+cpu --pre --extra-index-url https://fair.pkg.atmeta.com/fairseq2/whl/rc/pt2.5.1/cpu/ - python -m pip install fairseq2==0.3.0rc1 --pre --extra-index-url https://fair.pkg.atmeta.com/fairseq2/whl/rc/pt2.5.1/cpu/ - python -m pip install -r requirements.txt - python -m pip install -r requirements-dev.txt - python -m pip install -e . - - name: mypy - run: mypy --install-types --non-interactive ./ --cache-dir=.mypy_cache/ - - unit_test: + - uses: actions/checkout@v4 + - uses: ./.github/actions/setup + - run: uvx ruff check . + formatting: runs-on: ubuntu-latest - timeout-minutes: 20 - strategy: - fail-fast: false - matrix: - python-version: ["3.8"] + needs: [lock_file] steps: - - uses: actions/checkout@v3 - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v3 - with: - python-version: ${{ matrix.python-version }} - cache: "pip" - - name: Install dependencies - run: | - sudo apt-get install libsndfile1 - python -m pip install fairseq2n==0.3.0rc1+cpu --pre --extra-index-url https://fair.pkg.atmeta.com/fairseq2/whl/rc/pt2.5.1/cpu/ - python -m pip install fairseq2==0.3.0rc1 --pre --extra-index-url https://fair.pkg.atmeta.com/fairseq2/whl/rc/pt2.5.1/cpu/ - python -m pip install -r requirements.txt - python -m pip install -r requirements-dev.txt - python -m pip install -e . - - name: pytest_unit - run: pytest -s -v tests/unit_tests/ - - integration_test: + - uses: actions/checkout@v4 + - uses: ./.github/actions/setup + - run: uvx ruff check --select I . + - run: uvx ruff format --check . + type_consistency: runs-on: ubuntu-latest - strategy: - fail-fast: false - matrix: - python-version: ["3.8"] - timeout-minutes: 40 + needs: [lock_file] steps: - - uses: actions/checkout@v3 - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v3 - with: - python-version: ${{ matrix.python-version }} - cache: "pip" - - name: Install dependencies - run: | - sudo apt-get install libsndfile1 - python -m pip install --upgrade pip - python -m pip install fairseq2n==0.3.0rc1+cpu --pre --extra-index-url https://fair.pkg.atmeta.com/fairseq2/whl/rc/pt2.5.1/cpu/ - python -m pip install fairseq2==0.3.0rc1 --pre --extra-index-url https://fair.pkg.atmeta.com/fairseq2/whl/rc/pt2.5.1/cpu/ - python -m pip install -r requirements.txt - python -m pip install -r requirements-dev.txt - python -m pip install -e . - - name: Free Disk Space (Ubuntu) - uses: jlumbroso/free-disk-space@main - with: - # this might remove tools that are actually needed, - # if set to "true" but frees about 6 GB - tool-cache: false - - # all of these default to true, but feel free to set to - # "false" if necessary for your workflow - android: true - dotnet: true - haskell: true - large-packages: true - docker-images: true - - name: pytest_integration - run: pytest -s -v tests/integration_tests/ --ignore-glob='*sonar*.py' + - uses: actions/checkout@v4 + - uses: ./.github/actions/setup + - run: uvx --with=types-PyYAML mypy + tests: + runs-on: ubuntu-latest + needs: [lock_file] + steps: + - uses: actions/checkout@v4 + - uses: ./.github/actions/setup + - run: uv run --extra cpu pytest -s -v tests/unit_tests/ + build: + runs-on: ubuntu-latest + needs: [lock_file, linting, formatting, type_consistency, tests] + steps: + - uses: actions/checkout@v4 + - uses: ./.github/actions/setup + - run: uv build \ No newline at end of file diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..f287b16 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,16 @@ +repos: + - repo: https://github.com/astral-sh/uv-pre-commit + rev: 0.5.7 + hooks: + - id: uv-lock + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.8.2 + hooks: + # Lint + - id: ruff + args: [ --fix ] + # sort imports + - id: ruff + args: ["check", "--select", "I", "--fix"] + # format + - id: ruff-format \ No newline at end of file diff --git a/examples/bilingual_document.ipynb b/examples/bilingual_document.ipynb index c47a429..e587e80 100644 --- a/examples/bilingual_document.ipynb +++ b/examples/bilingual_document.ipynb @@ -26,22 +26,22 @@ "source": [ "%pip install --quiet sonar-space scikit-learn fastdtw pandas matplotlib seaborn sentence-splitter\n", "\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import torch\n", + "from fastdtw import fastdtw\n", + "from scipy.spatial.distance import cosine\n", + "from sentence_splitter import SentenceSplitter\n", + "from sklearn.metrics.pairwise import cosine_similarity\n", + "\n", "from sonar.inference_pipelines.text import TextToEmbeddingModelPipeline\n", "from sonar.models.sonar_text import (\n", " load_sonar_text_encoder_model,\n", " load_sonar_tokenizer,\n", ")\n", "\n", - "from sklearn.metrics.pairwise import cosine_similarity\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "from pprint import pprint\n", - "from sentence_splitter import SentenceSplitter\n", - "from scipy.spatial.distance import cosine\n", - "from fastdtw import fastdtw\n", - "\n", - "import torch\n", "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")" ] }, @@ -51,15 +51,8 @@ "metadata": {}, "outputs": [], "source": [ - "from sklearn.metrics.pairwise import cosine_similarity\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "from pprint import pprint\n", - "from sentence_splitter import SentenceSplitter\n", - "from scipy.spatial.distance import cosine\n", - "\n", "import torch\n", + "\n", "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")" ] }, @@ -69,24 +62,24 @@ "metadata": {}, "outputs": [], "source": [ - "eng_splitter = SentenceSplitter(language='en')\n", - "spa_splitter = SentenceSplitter(language='es')\n", - "deu_splitter = SentenceSplitter(language='de')\n", + "eng_splitter = SentenceSplitter(language=\"en\")\n", + "spa_splitter = SentenceSplitter(language=\"es\")\n", + "deu_splitter = SentenceSplitter(language=\"de\")\n", "\n", - "eng_text = ''\n", - "spa_text = ''\n", - "deu_text = ''\n", + "eng_text = \"\"\n", + "spa_text = \"\"\n", + "deu_text = \"\"\n", "\n", - "with open('./data/metamor_eng.txt', 'r') as file:\n", + "with open(\"./data/metamor_eng.txt\", \"r\") as file:\n", " eng_text = file.read()\n", - "with open('./data/metamor_spa.txt', 'r') as file:\n", + "with open(\"./data/metamor_spa.txt\", \"r\") as file:\n", " spa_text = file.read()\n", - "with open('./data/metamor_deu.txt', 'r') as file:\n", + "with open(\"./data/metamor_deu.txt\", \"r\") as file:\n", " deu_text = file.read()\n", "\n", "eng_sentences = eng_splitter.split(text=eng_text)\n", "spa_sentences = spa_splitter.split(text=spa_text)\n", - "deu_sentences = deu_splitter.split(text=deu_text)\n" + "deu_sentences = deu_splitter.split(text=deu_text)" ] }, { @@ -104,10 +97,14 @@ } ], "source": [ - "text_encoder_model = load_sonar_text_encoder_model(\"text_sonar_basic_encoder\", device=device).eval()\n", + "text_encoder_model = load_sonar_text_encoder_model(\n", + " \"text_sonar_basic_encoder\", device=device\n", + ").eval()\n", "text_tokenizer = load_sonar_tokenizer(\"text_sonar_basic_encoder\")\n", "\n", - "text_embedding_pipeline = TextToEmbeddingModelPipeline(text_encoder_model, text_tokenizer)" + "text_embedding_pipeline = TextToEmbeddingModelPipeline(\n", + " text_encoder_model, text_tokenizer\n", + ")" ] }, { @@ -171,14 +168,24 @@ } ], "source": [ - "eng_spa_df = pd.DataFrame(eng_spa_cos_sim, index=[f\"Eng {i}\" for i in range(len(eng_sentences))],\n", - " columns=[f\"Spa {i}\" for i in range(len(spa_sentences))])\n", + "eng_spa_df = pd.DataFrame(\n", + " eng_spa_cos_sim,\n", + " index=[f\"Eng {i}\" for i in range(len(eng_sentences))],\n", + " columns=[f\"Spa {i}\" for i in range(len(spa_sentences))],\n", + ")\n", "\n", - "eng_deu_df = pd.DataFrame(eng_deu_cos_sim, index=[f\"Eng {i}\" for i in range(len(eng_sentences))],\n", - " columns=[f\"Deu {i}\" for i in range(len(deu_sentences))])\n", + "eng_deu_df = pd.DataFrame(\n", + " eng_deu_cos_sim,\n", + " index=[f\"Eng {i}\" for i in range(len(eng_sentences))],\n", + " columns=[f\"Deu {i}\" for i in range(len(deu_sentences))],\n", + ")\n", + "\n", + "spa_deu_df = pd.DataFrame(\n", + " spa_deu_cos_sim,\n", + " index=[f\"Spa {i}\" for i in range(len(spa_sentences))],\n", + " columns=[f\"Deu {i}\" for i in range(len(deu_sentences))],\n", + ")\n", "\n", - "spa_deu_df = pd.DataFrame(spa_deu_cos_sim, index=[f\"Spa {i}\" for i in range(len(spa_sentences))],\n", - " columns=[f\"Deu {i}\" for i in range(len(deu_sentences))])\n", "\n", "def plot_heatmap(similarity_df, title):\n", " plt.figure(figsize=(10, 8))\n", @@ -186,6 +193,7 @@ " plt.title(title)\n", " plt.show()\n", "\n", + "\n", "plot_heatmap(eng_spa_df, \"English-Spanish Cosine Similarity\")\n", "plot_heatmap(eng_deu_df, \"English-German Cosine Similarity\")\n", "plot_heatmap(spa_deu_df, \"Spanish-German Cosine Similarity\")" @@ -309,9 +317,14 @@ } ], "source": [ - "def create_cosine_similarity_heatmap(cos_sim_matrix, sent1_list, sent2_list, lang1_name, lang2_name, threshold=0.6):\n", - " df = pd.DataFrame(cos_sim_matrix, index=[f\"{lang1_name}_{i}\" for i in range(len(sent1_list))], \n", - " columns=[f\"{lang2_name}_{i}\" for i in range(len(sent2_list))])\n", + "def create_cosine_similarity_heatmap(\n", + " cos_sim_matrix, sent1_list, sent2_list, lang1_name, lang2_name, threshold=0.6\n", + "):\n", + " df = pd.DataFrame(\n", + " cos_sim_matrix,\n", + " index=[f\"{lang1_name}_{i}\" for i in range(len(sent1_list))],\n", + " columns=[f\"{lang2_name}_{i}\" for i in range(len(sent2_list))],\n", + " )\n", "\n", " mask = df < threshold\n", "\n", @@ -319,18 +332,38 @@ " for i in range(len(sent1_list)):\n", " for j in range(len(sent2_list)):\n", " if df.iat[i, j] >= threshold:\n", - " print(f\"{lang1_name} Sentence {i}: {sent1_list[i]} <-> {lang2_name} Sentence {j}: {sent2_list[j]} (Cosine Similarity: {df.iat[i, j]:.2f})\")\n", - " \n", + " print(\n", + " f\"{lang1_name} Sentence {i}: {sent1_list[i]} <-> {lang2_name} Sentence {j}: {sent2_list[j]} (Cosine Similarity: {df.iat[i, j]:.2f})\"\n", + " )\n", + "\n", " plt.figure(figsize=(10, 8))\n", - " sns.heatmap(df, mask=mask, annot=False, cmap=\"coolwarm\", cbar=True, linewidths=0.5, vmin=0, vmax=1)\n", - " plt.title(f\"Cosine Similarity Heatmap: {lang1_name} -> {lang2_name} (Threshold > {threshold})\")\n", + " sns.heatmap(\n", + " df,\n", + " mask=mask,\n", + " annot=False,\n", + " cmap=\"coolwarm\",\n", + " cbar=True,\n", + " linewidths=0.5,\n", + " vmin=0,\n", + " vmax=1,\n", + " )\n", + " plt.title(\n", + " f\"Cosine Similarity Heatmap: {lang1_name} -> {lang2_name} (Threshold > {threshold})\"\n", + " )\n", " plt.xlabel(f\"{lang2_name} Sentences\")\n", " plt.ylabel(f\"{lang1_name} Sentences\")\n", " plt.show()\n", "\n", - "create_cosine_similarity_heatmap(eng_spa_cos_sim, eng_sentences, spa_sentences, \"English\", \"Spanish\", threshold=0.7)\n", - "create_cosine_similarity_heatmap(eng_deu_cos_sim, eng_sentences, deu_sentences, \"English\", \"German\", threshold=0.7)\n", - "create_cosine_similarity_heatmap(spa_deu_cos_sim, spa_sentences, deu_sentences, \"Spanish\", \"German\", threshold=0.7)\n" + "\n", + "create_cosine_similarity_heatmap(\n", + " eng_spa_cos_sim, eng_sentences, spa_sentences, \"English\", \"Spanish\", threshold=0.7\n", + ")\n", + "create_cosine_similarity_heatmap(\n", + " eng_deu_cos_sim, eng_sentences, deu_sentences, \"English\", \"German\", threshold=0.7\n", + ")\n", + "create_cosine_similarity_heatmap(\n", + " spa_deu_cos_sim, spa_sentences, deu_sentences, \"Spanish\", \"German\", threshold=0.7\n", + ")" ] }, { @@ -642,12 +675,15 @@ "source": [ "def dtw_align(eng_embeddings, spa_embeddings):\n", " alignments = []\n", - " \n", - " distance, path = fastdtw(eng_embeddings.numpy(), spa_embeddings.numpy(), dist=cosine)\n", + "\n", + " distance, path = fastdtw(\n", + " eng_embeddings.numpy(), spa_embeddings.numpy(), dist=cosine\n", + " )\n", " alignments.append(path)\n", - " \n", + "\n", " return alignments\n", "\n", + "\n", "eng_spa_alignments = dtw_align(eng_embeddings, spa_embeddings)\n", "\n", "\n", @@ -655,22 +691,24 @@ "\n", "alignment_table = []\n", "\n", - "for (eng_idx, spa_idx) in eng_spa_alignments[0]:\n", + "for eng_idx, spa_idx in eng_spa_alignments[0]:\n", " eng_sentence = eng_sentences[eng_idx] if eng_idx < len(eng_sentences) else \"\"\n", " spa_sentence = spa_sentences[spa_idx] if spa_idx < len(spa_sentences) else \"\"\n", - " \n", + "\n", " alignment_table.append((eng_sentence, spa_sentence))\n", "\n", - "bilingual_df = pd.DataFrame(alignment_table, columns=[\"English Sentence\", \"Spanish Sentence\"])\n", - "pd.set_option('display.max_colwidth', None)\n", + "bilingual_df = pd.DataFrame(\n", + " alignment_table, columns=[\"English Sentence\", \"Spanish Sentence\"]\n", + ")\n", + "pd.set_option(\"display.max_colwidth\", None)\n", "\n", "html_table = bilingual_df.to_html(index=False)\n", "with open(\"bilingual_book.html\", \"w\") as f:\n", " f.write(html_table)\n", "\n", - "from IPython.display import display, HTML\n", - "display(HTML(bilingual_df.to_html(index=False)))\n", - "\n" + "from IPython.display import HTML, display\n", + "\n", + "display(HTML(bilingual_df.to_html(index=False)))" ] }, { diff --git a/examples/inference_pipelines.ipynb b/examples/inference_pipelines.ipynb index c981864..cc7acfb 100644 --- a/examples/inference_pipelines.ipynb +++ b/examples/inference_pipelines.ipynb @@ -44,6 +44,7 @@ "outputs": [], "source": [ "import torch\n", + "\n", "device = torch.device(\"cpu\")" ] }, @@ -74,7 +75,9 @@ "metadata": {}, "outputs": [], "source": [ - "speech_encoder_model = load_sonar_speech_model(\"sonar_speech_encoder_eng\", device=device).eval()" + "speech_encoder_model = load_sonar_speech_model(\n", + " \"sonar_speech_encoder_eng\", device=device\n", + ").eval()" ] }, { @@ -83,7 +86,9 @@ "metadata": {}, "outputs": [], "source": [ - "text_encoder_model = load_sonar_text_encoder_model(\"text_sonar_basic_encoder\", device=device).eval()" + "text_encoder_model = load_sonar_text_encoder_model(\n", + " \"text_sonar_basic_encoder\", device=device\n", + ").eval()" ] }, { @@ -92,7 +97,9 @@ "metadata": {}, "outputs": [], "source": [ - "text_decoder_model = load_sonar_text_decoder_model(\"text_sonar_basic_decoder\", device=device).eval()" + "text_decoder_model = load_sonar_text_decoder_model(\n", + " \"text_sonar_basic_decoder\", device=device\n", + ").eval()" ] }, { @@ -118,7 +125,10 @@ "metadata": {}, "outputs": [], "source": [ - "from sonar.inference_pipelines.speech import SpeechToEmbeddingModelPipeline, SpeechToTextModelPipeline" + "from sonar.inference_pipelines.speech import (\n", + " SpeechToEmbeddingModelPipeline,\n", + " SpeechToTextModelPipeline,\n", + ")" ] }, { @@ -142,8 +152,12 @@ ], "source": [ "s2vec_model = SpeechToEmbeddingModelPipeline(encoder=speech_encoder_model)\n", - "s2vec_model.predict([\"../tests/integration_tests/data/audio_files/audio_1.wav\",\n", - " \"../tests/integration_tests/data/audio_files/audio_2.wav\"])" + "s2vec_model.predict(\n", + " [\n", + " \"../tests/integration_tests/data/audio_files/audio_1.wav\",\n", + " \"../tests/integration_tests/data/audio_files/audio_2.wav\",\n", + " ]\n", + ")" ] }, { @@ -164,12 +178,17 @@ } ], "source": [ - "s2t_model = SpeechToTextModelPipeline(encoder=speech_encoder_model,\n", - " decoder=text_decoder_model,\n", - " tokenizer=text_tokenizer)\n", + "s2t_model = SpeechToTextModelPipeline(\n", + " encoder=speech_encoder_model, decoder=text_decoder_model, tokenizer=text_tokenizer\n", + ")\n", "\n", - "s2t_model.predict([\"../tests/integration_tests/data/audio_files/audio_1.wav\",\n", - " \"../tests/integration_tests/data/audio_files/audio_2.wav\"], target_lang=\"eng_Latn\")" + "s2t_model.predict(\n", + " [\n", + " \"../tests/integration_tests/data/audio_files/audio_1.wav\",\n", + " \"../tests/integration_tests/data/audio_files/audio_2.wav\",\n", + " ],\n", + " target_lang=\"eng_Latn\",\n", + ")" ] }, { @@ -185,7 +204,10 @@ "metadata": {}, "outputs": [], "source": [ - "from sonar.inference_pipelines.text import TextToTextModelPipeline, TextToEmbeddingModelPipeline" + "from sonar.inference_pipelines.text import (\n", + " TextToEmbeddingModelPipeline,\n", + " TextToTextModelPipeline,\n", + ")" ] }, { @@ -194,8 +216,12 @@ "metadata": {}, "outputs": [], "source": [ - "text_embedding_pipeline = TextToEmbeddingModelPipeline(text_encoder_model, text_tokenizer)\n", - "text_to_text_pipeline = TextToTextModelPipeline(text_encoder_model, text_decoder_model, text_tokenizer)" + "text_embedding_pipeline = TextToEmbeddingModelPipeline(\n", + " text_encoder_model, text_tokenizer\n", + ")\n", + "text_to_text_pipeline = TextToTextModelPipeline(\n", + " text_encoder_model, text_decoder_model, text_tokenizer\n", + ")" ] }, { @@ -266,7 +292,9 @@ } ], "source": [ - "text_translation = text_to_text_pipeline.predict(data_source, source_lang=\"eng_Latn\", target_lang=\"fra_Latn\")\n", + "text_translation = text_to_text_pipeline.predict(\n", + " data_source, source_lang=\"eng_Latn\", target_lang=\"fra_Latn\"\n", + ")\n", "text_translation" ] } diff --git a/examples/mutox_example.ipynb b/examples/mutox_example.ipynb index 4cbe982..1019482 100644 --- a/examples/mutox_example.ipynb +++ b/examples/mutox_example.ipynb @@ -28,9 +28,10 @@ "metadata": {}, "outputs": [], "source": [ - "import torch\n", "from pathlib import Path\n", "\n", + "import torch\n", + "\n", "if torch.cuda.is_available():\n", " device = torch.device(\"cuda:0\")\n", " dtype = torch.float16\n", @@ -63,22 +64,28 @@ "outputs": [], "source": [ "# get demo file\n", - "import urllib.request\n", "import tempfile\n", + "import urllib.request\n", "\n", "files = [\n", - " (\"https://dl.fbaipublicfiles.com/seamless/tests/commonvoice_example_en_clocks.wav\", \"commonvoice_example_en_clocks.wav\"),\n", - " (\"https://dl.fbaipublicfiles.com/seamlessM4T/LJ037-0171_sr16k.wav\", \"LJ037-0171_sr16k.wav\")\n", + " (\n", + " \"https://dl.fbaipublicfiles.com/seamless/tests/commonvoice_example_en_clocks.wav\",\n", + " \"commonvoice_example_en_clocks.wav\",\n", + " ),\n", + " (\n", + " \"https://dl.fbaipublicfiles.com/seamlessM4T/LJ037-0171_sr16k.wav\",\n", + " \"LJ037-0171_sr16k.wav\",\n", + " ),\n", "]\n", "\n", "tmpdir = Path(tempfile.mkdtemp())\n", - "tsv_file = (tmpdir / 'data.tsv')\n", - "with tsv_file.open('w') as tsv_file_p:\n", - " print('path', file=tsv_file_p)\n", - " for (uri, name) in files:\n", + "tsv_file = tmpdir / \"data.tsv\"\n", + "with tsv_file.open(\"w\") as tsv_file_p:\n", + " print(\"path\", file=tsv_file_p)\n", + " for uri, name in files:\n", " dl = tmpdir / name\n", " urllib.request.urlretrieve(uri, dl)\n", - " print(dl, file=tsv_file_p)\n" + " print(dl, file=tsv_file_p)" ] }, { @@ -87,12 +94,12 @@ "metadata": {}, "outputs": [], "source": [ - "from sonar.inference_pipelines.speech import SpeechInferenceParams\n", "from sonar.inference_pipelines.mutox_speech import MutoxSpeechClassifierPipeline\n", + "from sonar.inference_pipelines.speech import SpeechInferenceParams\n", "\n", "pipeline_builder = MutoxSpeechClassifierPipeline.load_model_from_name(\n", - " mutox_classifier_name =\"sonar_mutox\",\n", - " encoder_name=f\"sonar_speech_encoder_eng\",\n", + " mutox_classifier_name=\"sonar_mutox\",\n", + " encoder_name=\"sonar_speech_encoder_eng\",\n", " device=device,\n", ")" ] @@ -103,17 +110,19 @@ "metadata": {}, "outputs": [], "source": [ - "pipeline = pipeline_builder.build_pipeline(SpeechInferenceParams(\n", - " data_file=tsv_file,\n", - " audio_root_dir=None,\n", - " audio_path_index=0,\n", - " target_lang=\"eng\",\n", - " batch_size=4,\n", - " pad_idx=0,\n", - " device=device,\n", - " fbank_dtype=torch.float32,\n", - " n_parallel=4\n", - "))" + "pipeline = pipeline_builder.build_pipeline(\n", + " SpeechInferenceParams(\n", + " data_file=tsv_file,\n", + " audio_root_dir=None,\n", + " audio_path_index=0,\n", + " target_lang=\"eng\",\n", + " batch_size=4,\n", + " pad_idx=0,\n", + " device=device,\n", + " fbank_dtype=torch.float32,\n", + " n_parallel=4,\n", + " )\n", + ")" ] }, { @@ -139,9 +148,9 @@ ], "source": [ "for batch in pipeline:\n", - " ex = batch['audio']\n", - " for idx, path in enumerate(ex['path']):\n", - " print(str(path), ex[\"data\"][idx].item(), sep=\"\\t\")\n" + " ex = batch[\"audio\"]\n", + " for idx, path in enumerate(ex[\"path\"]):\n", + " print(str(path), ex[\"data\"][idx].item(), sep=\"\\t\")" ] }, { @@ -152,6 +161,7 @@ "source": [ "# cleanup tmp dir\n", "import shutil\n", + "\n", "shutil.rmtree(tmpdir)" ] }, @@ -181,15 +191,15 @@ } ], "source": [ - "from sonar.models.mutox.loader import load_mutox_model\n", "from sonar.inference_pipelines.text import TextToEmbeddingModelPipeline\n", + "from sonar.models.mutox.loader import load_mutox_model\n", "\n", "t2vec_model = TextToEmbeddingModelPipeline(\n", " encoder=\"text_sonar_basic_encoder\",\n", " tokenizer=\"text_sonar_basic_encoder\",\n", " device=device,\n", ")\n", - "text_column='lang_txt'\n", + "text_column = \"lang_txt\"\n", "classifier = load_mutox_model(\n", " \"sonar_mutox\",\n", " device=device,\n", @@ -215,7 +225,10 @@ ], "source": [ "with torch.inference_mode():\n", - " emb = t2vec_model.predict([\"De peur que le pays ne se prostitue et ne se remplisse de crimes.\"], source_lang='fra_Latn')\n", + " emb = t2vec_model.predict(\n", + " [\"De peur que le pays ne se prostitue et ne se remplisse de crimes.\"],\n", + " source_lang=\"fra_Latn\",\n", + " )\n", " x = classifier(emb.to(device).half())\n", "\n", "x" diff --git a/examples/sonar_text_demo.ipynb b/examples/sonar_text_demo.ipynb index 62866bf..c793d74 100644 --- a/examples/sonar_text_demo.ipynb +++ b/examples/sonar_text_demo.ipynb @@ -30,7 +30,11 @@ "outputs": [], "source": [ "import torch\n", - "from sonar.inference_pipelines.text import TextToTextModelPipeline, TextToEmbeddingModelPipeline" + "\n", + "from sonar.inference_pipelines.text import (\n", + " TextToEmbeddingModelPipeline,\n", + " TextToTextModelPipeline,\n", + ")" ] }, { @@ -39,11 +43,14 @@ "metadata": {}, "outputs": [], "source": [ - "text2vec = TextToEmbeddingModelPipeline(encoder=\"text_sonar_basic_encoder\",\n", - " tokenizer=\"text_sonar_basic_encoder\")\n", - "text2text = TextToTextModelPipeline(encoder=\"text_sonar_basic_encoder\",\n", - " decoder=\"text_sonar_basic_decoder\",\n", - " tokenizer=\"text_sonar_basic_encoder\") # tokenizer is attached to both encoder and decoder cards" + "text2vec = TextToEmbeddingModelPipeline(\n", + " encoder=\"text_sonar_basic_encoder\", tokenizer=\"text_sonar_basic_encoder\"\n", + ")\n", + "text2text = TextToTextModelPipeline(\n", + " encoder=\"text_sonar_basic_encoder\",\n", + " decoder=\"text_sonar_basic_decoder\",\n", + " tokenizer=\"text_sonar_basic_encoder\",\n", + ") # tokenizer is attached to both encoder and decoder cards" ] }, { @@ -62,37 +69,41 @@ "outputs": [], "source": [ "# shorted examples from FLORES dataset\n", - "english_sentences = ['He died in Osaka on Tuesday.',\n", - " 'No one was inside the apartment.',\n", - " 'Zayat was unhurt in the accident.',\n", - " '\"Some may not agree but I don\\'t care.',\n", - " 'He documented himself in a 1998 book.',\n", - " 'An inquiry was established to investigate.',\n", - " 'Then Whirling Dervishes took to the stage.',\n", - " 'In Berlin, police estimated 6,500 protestors.',\n", - " 'He joined them in 1945 and stayed until 1958.',\n", - " \"It is the biggest acquisition in eBay's history.\",\n", - " '\"We were all simply in shock,\" the mother stated.',\n", - " 'Unwanted murals or scribble is known as graffiti.',\n", - " 'Seven points behind, Johnson is second with 2,243.',\n", - " 'When the official arrived, the apartment exploded.',\n", - " 'A Hangi Meal is cooked in a hot pit in the ground.']\n", + "english_sentences = [\n", + " \"He died in Osaka on Tuesday.\",\n", + " \"No one was inside the apartment.\",\n", + " \"Zayat was unhurt in the accident.\",\n", + " \"\\\"Some may not agree but I don't care.\",\n", + " \"He documented himself in a 1998 book.\",\n", + " \"An inquiry was established to investigate.\",\n", + " \"Then Whirling Dervishes took to the stage.\",\n", + " \"In Berlin, police estimated 6,500 protestors.\",\n", + " \"He joined them in 1945 and stayed until 1958.\",\n", + " \"It is the biggest acquisition in eBay's history.\",\n", + " '\"We were all simply in shock,\" the mother stated.',\n", + " \"Unwanted murals or scribble is known as graffiti.\",\n", + " \"Seven points behind, Johnson is second with 2,243.\",\n", + " \"When the official arrived, the apartment exploded.\",\n", + " \"A Hangi Meal is cooked in a hot pit in the ground.\",\n", + "]\n", "\n", - "french_sentences = ['Il est mort à Osaka mardi.',\n", - " \"Personne n'était à l'intérieur de l'appartement.\",\n", - " \"Zayat est sorti indemne de l'accident.\",\n", - " \"«\\xa0Certains ne seront peut-être pas d'accord, mais cela m'est égal.\",\n", - " 'Il a fait des recherches dans un livre paru en 1998.',\n", - " 'Une enquête a été ouverte.',\n", - " 'Puis les derviches tourneurs sont montés sur scène.',\n", - " \"À Berlin, la police estime qu'il y avait environ 6 500 manifestants.\",\n", - " \"Il le rejoint en 1945 et y reste jusqu'en 1958.\",\n", - " 'C’est la plus grande acquisition de l’histoire d’eBay.',\n", - " '« Nous étions tous simplement sous le choc », a déclaré la mère.',\n", - " 'Les peintures murales ou les gribouillages indésirables sont connus sous le nom de graffitis.',\n", - " 'Sept points de retard, Johnson est deuxième avec 2243 points.',\n", - " \"Une fois l'agent arrivé sur place, l'appartement a explosé.\",\n", - " 'On cuit un repas hangi dans une fosse chaude creusée dans le sol.']" + "french_sentences = [\n", + " \"Il est mort à Osaka mardi.\",\n", + " \"Personne n'était à l'intérieur de l'appartement.\",\n", + " \"Zayat est sorti indemne de l'accident.\",\n", + " \"«\\xa0Certains ne seront peut-être pas d'accord, mais cela m'est égal.\",\n", + " \"Il a fait des recherches dans un livre paru en 1998.\",\n", + " \"Une enquête a été ouverte.\",\n", + " \"Puis les derviches tourneurs sont montés sur scène.\",\n", + " \"À Berlin, la police estime qu'il y avait environ 6 500 manifestants.\",\n", + " \"Il le rejoint en 1945 et y reste jusqu'en 1958.\",\n", + " \"C’est la plus grande acquisition de l’histoire d’eBay.\",\n", + " \"« Nous étions tous simplement sous le choc », a déclaré la mère.\",\n", + " \"Les peintures murales ou les gribouillages indésirables sont connus sous le nom de graffitis.\",\n", + " \"Sept points de retard, Johnson est deuxième avec 2243 points.\",\n", + " \"Une fois l'agent arrivé sur place, l'appartement a explosé.\",\n", + " \"On cuit un repas hangi dans une fosse chaude creusée dans le sol.\",\n", + "]" ] }, { @@ -143,13 +154,14 @@ ], "source": [ "#!pip install seaborn pandas\n", - "import seaborn as sns\n", "import pandas as pd\n", + "import seaborn as sns\n", + "\n", "cos_sim = embed_english_sentence.dot(embed_french_sentence.T)\n", "sim_df = pd.DataFrame(cos_sim, columns=french_sentences, index=english_sentences)\n", - "sns.set(font_scale=.8)\n", - "sns.set (rc = {'figure.figsize':(10, 10)})\n", - "sns.heatmap(sim_df, cmap=\"vlag\", linewidths=.5)" + "sns.set(font_scale=0.8)\n", + "sns.set(rc={\"figure.figsize\": (10, 10)})\n", + "sns.heatmap(sim_df, cmap=\"vlag\", linewidths=0.5)" ] }, { @@ -165,13 +177,13 @@ "metadata": {}, "outputs": [], "source": [ - "french_translated_sentences = text2text.predict(english_sentences,\n", - " source_lang=\"eng_Latn\", \n", - " target_lang=\"fra_Latn\")\n", + "french_translated_sentences = text2text.predict(\n", + " english_sentences, source_lang=\"eng_Latn\", target_lang=\"fra_Latn\"\n", + ")\n", "\n", - "english_translated_sentences = text2text.predict(french_sentences,\n", - " source_lang=\"fra_Latn\", \n", - " target_lang=\"eng_Latn\")" + "english_translated_sentences = text2text.predict(\n", + " french_sentences, source_lang=\"fra_Latn\", target_lang=\"eng_Latn\"\n", + ")" ] }, { @@ -201,17 +213,21 @@ } ], "source": [ - "\n", "import sacrebleu\n", - "bleu_obj = sacrebleu.corpus_bleu(french_translated_sentences, [french_sentences], tokenize='flores200')\n", + "\n", + "bleu_obj = sacrebleu.corpus_bleu(\n", + " french_translated_sentences, [french_sentences], tokenize=\"flores200\"\n", + ")\n", "print(\"*\" * 120)\n", "print(\"english to french translation bleu score :\")\n", "print(bleu_obj)\n", "\n", - "bleu_obj = sacrebleu.corpus_bleu(english_translated_sentences, [english_sentences], tokenize='flores200')\n", + "bleu_obj = sacrebleu.corpus_bleu(\n", + " english_translated_sentences, [english_sentences], tokenize=\"flores200\"\n", + ")\n", "print(\"*\" * 120)\n", "print(\"french to english translation bleu score :\")\n", - "print(bleu_obj)\n" + "print(bleu_obj)" ] }, { diff --git a/huggingface_pipelines/audio.py b/huggingface_pipelines/audio.py index 3341653..561e4bd 100644 --- a/huggingface_pipelines/audio.py +++ b/huggingface_pipelines/audio.py @@ -246,15 +246,12 @@ def process_batch(self, batch: Dict[str, Any]) -> Dict[str, Any]: audio_inputs = self.collect_valid_audio_inputs(batch[column]) if not audio_inputs: - raise ValueError(f"No valid audio inputs found in column {column}/") try: - audio_embeddings: List[np.ndarray] = [] for i in range(0, len(audio_inputs), self.config.batch_size): - batch_inputs = [ tensor.to(self.config.device) for tensor in audio_inputs[i : i + self.config.batch_size] diff --git a/huggingface_pipelines/text.py b/huggingface_pipelines/text.py index 1a72c34..7aea67b 100644 --- a/huggingface_pipelines/text.py +++ b/huggingface_pipelines/text.py @@ -477,7 +477,6 @@ def process_batch(self, batch: Dict[str, Any]) -> Dict[str, Any]: """ for column in self.config.columns: if column in batch: - if not isinstance(batch[column], (list, np.ndarray)): raise ValueError( f"Expected list for column {column}, got {type(batch[column])}" diff --git a/pyproject.toml b/pyproject.toml index 7f20e08..fa899d4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -23,7 +23,6 @@ keywords = [ "multi-language models", ] - # zip_safe = false classifiers = [ "Topic :: Scientific/Engineering", @@ -31,10 +30,8 @@ classifiers = [ ] dependencies = [ -# "fairseq2>=0.3.0rc1", # install manually from https://fair.pkg.atmeta.com/fairseq2/whl/rc/pt2.5.1/cpu/ + "fairseq2>=0.3.0rc1", "numpy>=1.21", - "torch", - "torchaudio", "sox", "soundfile", "tqdm", @@ -42,22 +39,17 @@ dependencies = [ "typing_extensions", ] -[project.optional-dependencies] -dev = [ - # Test - "pytest>=4.3.0", - "pytest-asyncio>=0.15.0", - "pytest-cov>=2.6.1", - "coverage[toml]>=5.1", - # Format - "black==24.3.0", - "isort>=5.10.1", - # Linters - "mypy>=0.782", - "pylint>=2.8.0", -] +[dependency-groups] +dev = ["pytest-asyncio>=0.15.0", "pytest-cov>=2.6.1", "pytest>=4.3.0", "coverage[toml]>=5.1"] +[project.optional-dependencies] +cpu = [ + "torch==2.5.1+cpu", + "torchaudio", + "fairseq2n==0.3.0rc1", + "fairseq2[arrow]==0.3.0rc1", +] hg = [ "transformers>=4.44.0", "datasets>=2.20.0", @@ -67,7 +59,6 @@ hg = [ "absl-py>=2.1.0", ] - [project.urls] Source = "https://github.com/facebookresearch/SONAR" Tracker = "https://github.com/facebookresearch/SONAR/issues" @@ -78,27 +69,13 @@ Tracker = "https://github.com/facebookresearch/SONAR/issues" [tool.flit.module] name = "sonar" -[tool.black] +[tool.ruff] +target-version = "py38" line-length = 88 -include = '\.pyi?$' -# 'extend-exclude' excludes files or directories in addition to the defaults -extend-exclude = ''' -# A regex preceded with ^/ will apply only to files and directories -# in the root of the project. -( - ^/foo.py # exclude a file named foo.py in the root of the project - | .*_pb2.py # exclude autogenerated Protocol Buffer files anywhere in the project -) -''' - -[tool.flake8] -extend_ignore = ["E", "Y"] # Black -per-file-ignores = ["__init__.py:F401"] - -[tool.isort] -profile = "black" -skip_gitignore = true -skip_glob = [] + +[tool.ruff.lint] +ignore = ["E"] +per-file-ignores = {"__init__.py"=["F401"]} [tool.mypy] python_version = "3.8" @@ -111,3 +88,22 @@ files = ["sonar/"] minversion = "6.0" testpaths = ["tests/"] python_files = ["test_*.py"] + +[tool.uv.sources] +fairseq2 = [ + { index = "fairseq2-cpu", extra = 'cpu' } +] +fairseq2n = [ + { index = "fairseq2-cpu", extra = 'cpu' } +] +torch={index="pytorch-cpu"} + +[[tool.uv.index]] +name = "fairseq2-cpu" +url = "https://fair.pkg.atmeta.com/fairseq2/whl/rc/pt2.5.1/cpu/" +explicit = true + +[[tool.uv.index]] +name = "pytorch-cpu" +url = "https://download.pytorch.org/whl/cpu" +explicit = true \ No newline at end of file diff --git a/requirements-dev.txt b/requirements-dev.txt deleted file mode 100644 index 22dfe8d..0000000 --- a/requirements-dev.txt +++ /dev/null @@ -1,12 +0,0 @@ -# Test -pytest>=4.3.0 -pytest-asyncio>=0.15.0 -pytest-cov>=2.6.1 -coverage[toml]>=5.1 -# Format -black==24.3.0 -isort>=5.10.1 -# Linter -mypy>=0.782 -pylint>=2.8.0 -flake8 diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index 281cc2f..0000000 --- a/requirements.txt +++ /dev/null @@ -1,9 +0,0 @@ -# fairseq2>=0.3.0rc1 # install manually from https://fair.pkg.atmeta.com/fairseq2/whl/rc/pt2.5.1/cpu/ -# See https://github.com/NVIDIA/TensorRT-LLM/issues/1145 -mpmath==1.3.0 -numpy>=1.21 -torch -torchaudio -sox -soundfile -tqdm diff --git a/sonar/__init__.py b/sonar/__init__.py index ecb3129..506cf70 100644 --- a/sonar/__init__.py +++ b/sonar/__init__.py @@ -4,9 +4,8 @@ # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. -"""SONAR provides a set of speech and text encoders for multilingual, multimodal semantic embedding. +"""SONAR provides a set of speech and text encoders for multilingual, multimodal semantic embedding.""" -""" from fairseq2.assets import default_asset_store __version__ = "0.3.1" diff --git a/sonar/inference_pipelines/mutox_speech.py b/sonar/inference_pipelines/mutox_speech.py index 8e7ff71..9d990c4 100644 --- a/sonar/inference_pipelines/mutox_speech.py +++ b/sonar/inference_pipelines/mutox_speech.py @@ -14,7 +14,6 @@ AudioToFbankDataPipelineBuilder, SpeechInferenceParams, SpeechInferencePipeline, - SpeechToEmbeddingPipeline, ) from sonar.inference_pipelines.utils import extract_sequence_batch from sonar.models.encoder_model import SonarEncoderModel diff --git a/sonar/inference_pipelines/speech.py b/sonar/inference_pipelines/speech.py index 102a0a0..6053207 100644 --- a/sonar/inference_pipelines/speech.py +++ b/sonar/inference_pipelines/speech.py @@ -94,9 +94,7 @@ def prebuild_pipeline(self, context: SpeechInferenceParams) -> DataPipelineBuild # Start building the pipeline pipeline_builder = ( # Open TSV, skip the header line, split into fields, and return audio field - read_text(context.data_file, rtrim=True) - .skip(1) - .map(split_tsv) + read_text(context.data_file, rtrim=True).skip(1).map(split_tsv) ) # Memory map audio files and cache up to 10 files. diff --git a/sonar/inference_pipelines/utils.py b/sonar/inference_pipelines/utils.py index 6d26112..16fde62 100644 --- a/sonar/inference_pipelines/utils.py +++ b/sonar/inference_pipelines/utils.py @@ -11,12 +11,11 @@ from fairseq2.data import SequenceData from fairseq2.models.sequence import SequenceBatch from fairseq2.nn.padding import get_seqs_and_padding_mask -from fairseq2.typing import DataType, Device +from fairseq2.typing import Device from tqdm.auto import tqdm def extract_sequence_batch(x: SequenceData, device: Device) -> SequenceBatch: - seqs, padding_mask = get_seqs_and_padding_mask(x, device=device) return SequenceBatch(seqs, padding_mask) diff --git a/sonar/models/mutox/builder.py b/sonar/models/mutox/builder.py index b308cb9..0790bae 100644 --- a/sonar/models/mutox/builder.py +++ b/sonar/models/mutox/builder.py @@ -6,7 +6,6 @@ import typing as tp -import torch from fairseq2.typing import DataType, Device from torch import nn diff --git a/sonar/models/mutox/classifier.py b/sonar/models/mutox/classifier.py index 9fcdb3a..7f2565c 100644 --- a/sonar/models/mutox/classifier.py +++ b/sonar/models/mutox/classifier.py @@ -5,11 +5,9 @@ # MIT_LICENSE file in the root directory of this source tree. from dataclasses import dataclass -from typing import Optional import torch from fairseq2.config_registry import ConfigRegistry -from fairseq2.typing import DataType, Device from torch import nn diff --git a/sonar/models/mutox/loader.py b/sonar/models/mutox/loader.py index a1137d1..55776f9 100644 --- a/sonar/models/mutox/loader.py +++ b/sonar/models/mutox/loader.py @@ -10,7 +10,7 @@ from fairseq2.models.loader import StandardModelLoader, load_model from .builder import create_mutox_model -from .classifier import MutoxClassifier, MutoxConfig, mutox_archs +from .classifier import MutoxConfig, mutox_archs __import__("sonar") # Import only to update asset_store diff --git a/sonar/models/sonar_speech/loader.py b/sonar/models/sonar_speech/loader.py index 996311d..14fa27f 100644 --- a/sonar/models/sonar_speech/loader.py +++ b/sonar/models/sonar_speech/loader.py @@ -37,39 +37,37 @@ def convert_sonar_speech_checkpoint( key_map = { # fmt: off # encoder - r"^encoder.w2v_model.layer_norm\.": r"encoder_frontend.post_extract_layer_norm.", - r"^encoder.w2v_model.post_extract_proj\.": r"encoder_frontend.model_dim_proj.", - r"^encoder.w2v_model.encoder\.layers\.([0-9]+)\.conv_module\.batch_norm\.": r"encoder.layers.\1.conv.batch_norm.", - r"^encoder.w2v_model.encoder\.layers\.([0-9]+)\.conv_module\.depthwise_conv\.": r"encoder.layers.\1.conv.depthwise_conv.", - r"^encoder.w2v_model.encoder\.layers\.([0-9]+)\.conv_module\.layer_norm\.": r"encoder.layers.\1.conv_layer_norm.", + r"^encoder.w2v_model.layer_norm\.": r"encoder_frontend.post_extract_layer_norm.", + r"^encoder.w2v_model.post_extract_proj\.": r"encoder_frontend.model_dim_proj.", + r"^encoder.w2v_model.encoder\.layers\.([0-9]+)\.conv_module\.batch_norm\.": r"encoder.layers.\1.conv.batch_norm.", + r"^encoder.w2v_model.encoder\.layers\.([0-9]+)\.conv_module\.depthwise_conv\.": r"encoder.layers.\1.conv.depthwise_conv.", + r"^encoder.w2v_model.encoder\.layers\.([0-9]+)\.conv_module\.layer_norm\.": r"encoder.layers.\1.conv_layer_norm.", r"^encoder.w2v_model.encoder\.layers\.([0-9]+)\.conv_module\.pointwise_conv1\.": r"encoder.layers.\1.conv.pointwise_conv1.", r"^encoder.w2v_model.encoder\.layers\.([0-9]+)\.conv_module\.pointwise_conv2\.": r"encoder.layers.\1.conv.pointwise_conv2.", - r"^encoder.w2v_model.encoder\.layers\.([0-9]+)\.ffn(1|2)\.layer_norm\.": r"encoder.layers.\1.ffn\2_layer_norm.", - r"^encoder.w2v_model.encoder\.layers\.([0-9]+)\.ffn(1|2)\.w_1\.": r"encoder.layers.\1.ffn\2.inner_proj.", - r"^encoder.w2v_model.encoder\.layers\.([0-9]+)\.ffn(1|2)\.w_2\.": r"encoder.layers.\1.ffn\2.output_proj.", - r"^encoder.w2v_model.encoder\.layers\.([0-9]+)\.self_attn_layer_norm\.": r"encoder.layers.\1.self_attn_layer_norm.", - r"^encoder.w2v_model.encoder\.layers\.([0-9]+)\.self_attn\.linear_q\.": r"encoder.layers.\1.self_attn.q_proj.", - r"^encoder.w2v_model.encoder\.layers\.([0-9]+)\.self_attn\.linear_k\.": r"encoder.layers.\1.self_attn.k_proj.", - r"^encoder.w2v_model.encoder\.layers\.([0-9]+)\.self_attn\.linear_v\.": r"encoder.layers.\1.self_attn.v_proj.", - r"^encoder.w2v_model.encoder\.layers\.([0-9]+)\.self_attn\.linear_out\.": r"encoder.layers.\1.self_attn.output_proj.", - r"^encoder.w2v_model.encoder\.layers\.([0-9]+)\.self_attn\.linear_pos\.": r"encoder.layers.\1.self_attn.sdpa.r_proj.", - r"^encoder.w2v_model.encoder\.layers\.([0-9]+)\.self_attn\.pos_bias_u": r"encoder.layers.\1.self_attn.sdpa.u_bias", - r"^encoder.w2v_model.encoder\.layers\.([0-9]+)\.self_attn\.pos_bias_v": r"encoder.layers.\1.self_attn.sdpa.v_bias", - r"^encoder.w2v_model.encoder\.layers\.([0-9]+)\.final_layer_norm\.": r"encoder.layers.\1.layer_norm.", - r"^encoder.w2v_model.encoder\.layer_norm\.": r"encoder.layer_norm.", - - r"^decoder\.embed_tokens\.": r"encoder_pooler.decoder_frontend.embed.", - r"^decoder\.layers\.([0-9]+)\.self_attn_layer_norm\.": r"encoder_pooler.decoder.layers.\1.self_attn_layer_norm.", - r"^decoder\.layers\.([0-9]+)\.self_attn\.out_proj\.": r"encoder_pooler.decoder.layers.\1.self_attn.output_proj.", - r"^decoder\.layers\.([0-9]+)\.self_attn\.": r"encoder_pooler.decoder.layers.\1.self_attn.", + r"^encoder.w2v_model.encoder\.layers\.([0-9]+)\.ffn(1|2)\.layer_norm\.": r"encoder.layers.\1.ffn\2_layer_norm.", + r"^encoder.w2v_model.encoder\.layers\.([0-9]+)\.ffn(1|2)\.w_1\.": r"encoder.layers.\1.ffn\2.inner_proj.", + r"^encoder.w2v_model.encoder\.layers\.([0-9]+)\.ffn(1|2)\.w_2\.": r"encoder.layers.\1.ffn\2.output_proj.", + r"^encoder.w2v_model.encoder\.layers\.([0-9]+)\.self_attn_layer_norm\.": r"encoder.layers.\1.self_attn_layer_norm.", + r"^encoder.w2v_model.encoder\.layers\.([0-9]+)\.self_attn\.linear_q\.": r"encoder.layers.\1.self_attn.q_proj.", + r"^encoder.w2v_model.encoder\.layers\.([0-9]+)\.self_attn\.linear_k\.": r"encoder.layers.\1.self_attn.k_proj.", + r"^encoder.w2v_model.encoder\.layers\.([0-9]+)\.self_attn\.linear_v\.": r"encoder.layers.\1.self_attn.v_proj.", + r"^encoder.w2v_model.encoder\.layers\.([0-9]+)\.self_attn\.linear_out\.": r"encoder.layers.\1.self_attn.output_proj.", + r"^encoder.w2v_model.encoder\.layers\.([0-9]+)\.self_attn\.linear_pos\.": r"encoder.layers.\1.self_attn.sdpa.r_proj.", + r"^encoder.w2v_model.encoder\.layers\.([0-9]+)\.self_attn\.pos_bias_u": r"encoder.layers.\1.self_attn.sdpa.u_bias", + r"^encoder.w2v_model.encoder\.layers\.([0-9]+)\.self_attn\.pos_bias_v": r"encoder.layers.\1.self_attn.sdpa.v_bias", + r"^encoder.w2v_model.encoder\.layers\.([0-9]+)\.final_layer_norm\.": r"encoder.layers.\1.layer_norm.", + r"^encoder.w2v_model.encoder\.layer_norm\.": r"encoder.layer_norm.", + r"^decoder\.embed_tokens\.": r"encoder_pooler.decoder_frontend.embed.", + r"^decoder\.layers\.([0-9]+)\.self_attn_layer_norm\.": r"encoder_pooler.decoder.layers.\1.self_attn_layer_norm.", + r"^decoder\.layers\.([0-9]+)\.self_attn\.out_proj\.": r"encoder_pooler.decoder.layers.\1.self_attn.output_proj.", + r"^decoder\.layers\.([0-9]+)\.self_attn\.": r"encoder_pooler.decoder.layers.\1.self_attn.", r"^decoder\.layers\.([0-9]+)\.encoder_attn_layer_norm\.": r"encoder_pooler.decoder.layers.\1.encoder_decoder_attn_layer_norm.", - r"^decoder\.layers\.([0-9]+)\.encoder_attn\.out_proj\.": r"encoder_pooler.decoder.layers.\1.encoder_decoder_attn.output_proj.", - r"^decoder\.layers\.([0-9]+)\.encoder_attn\.": r"encoder_pooler.decoder.layers.\1.encoder_decoder_attn.", - r"^decoder\.layers\.([0-9]+)\.fc1\.": r"encoder_pooler.decoder.layers.\1.ffn.inner_proj.", - r"^decoder\.layers\.([0-9]+)\.fc2\.": r"encoder_pooler.decoder.layers.\1.ffn.output_proj.", - r"^decoder\.layers\.([0-9]+)\.final_layer_norm\.": r"encoder_pooler.decoder.layers.\1.ffn_layer_norm.", - - r"^decoder\.embed_out": r"encoder_pooler.projection_out.weight", + r"^decoder\.layers\.([0-9]+)\.encoder_attn\.out_proj\.": r"encoder_pooler.decoder.layers.\1.encoder_decoder_attn.output_proj.", + r"^decoder\.layers\.([0-9]+)\.encoder_attn\.": r"encoder_pooler.decoder.layers.\1.encoder_decoder_attn.", + r"^decoder\.layers\.([0-9]+)\.fc1\.": r"encoder_pooler.decoder.layers.\1.ffn.inner_proj.", + r"^decoder\.layers\.([0-9]+)\.fc2\.": r"encoder_pooler.decoder.layers.\1.ffn.output_proj.", + r"^decoder\.layers\.([0-9]+)\.final_layer_norm\.": r"encoder_pooler.decoder.layers.\1.ffn_layer_norm.", + r"^decoder\.embed_out": r"encoder_pooler.projection_out.weight", # fmt: on } diff --git a/sonar/models/sonar_text/loader.py b/sonar/models/sonar_text/loader.py index 295460c..c87f94b 100644 --- a/sonar/models/sonar_text/loader.py +++ b/sonar/models/sonar_text/loader.py @@ -20,8 +20,6 @@ sonar_text_decoder_archs, sonar_text_encoder_archs, ) -from sonar.models.sonar_text.model import SonarTextTransformerEncoderModel -from sonar.nn.conditional_decoder_model import ConditionalTransformerDecoderModel def convert_sonar_text_encoder_checkpoint( diff --git a/tests/unit_tests/huggingface_pipelines/audio.py b/tests/unit_tests/huggingface_pipelines/audio.py index e90cca6..674cb1f 100644 --- a/tests/unit_tests/huggingface_pipelines/audio.py +++ b/tests/unit_tests/huggingface_pipelines/audio.py @@ -1,5 +1,5 @@ from typing import Any, Dict -from unittest.mock import MagicMock, patch +from unittest.mock import patch import numpy as np import pytest diff --git a/tests/unit_tests/test_low_dimension_text_models.py b/tests/unit_tests/test_low_dimension_text_models.py index 9a4a047..06d7638 100644 --- a/tests/unit_tests/test_low_dimension_text_models.py +++ b/tests/unit_tests/test_low_dimension_text_models.py @@ -14,7 +14,6 @@ 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